Dataset Information

A high throughput method for quantifying number and size distribution of Arabidopsis seeds using large particle flow cytometry.

ABSTRACT: Background:Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as Arabidopsis thaliana, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods. Results:We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual seeds and sort them according to size, allowing an average of 12,000 seeds/hour to be processed. To achieve this high throughput, post harvested seeds are first separated from remaining plant material (dust and chaff) using a rapid sedimentation-based method. Then, classification algorithms are used to refine the separation process in silico. Accurate identification of all seeds in the samples was achieved, with relative errors below 2%. Conclusion:The tests performed reveal that there is no single classification algorithm that performs best for all samples, so the recommended strategy is to train and use multiple algorithms and use the median predictions of seed size and number across all algorithms. To facilitate the use of this method, an R package (SeedSorter) that implements the methodology has been developed and made freely available. The method was validated with seed samples from several natural accessions of Arabidopsis thaliana, but our analysis pipeline is applicable to any species with seed sizes smaller than 1.5 mm.

SUBMITTER: Morales A

PROVIDER: S-EPMC7053093 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A high throughput method for quantifying number and size distribution of <i>Arabidopsis</i> seeds using large particle flow cytometry.

Morales Alejandro A Teapal J J Ammerlaan J M H JMH Yin X X Evers J B JB Anten N P R NPR Sasidharan R R van Zanten M M

Plant methods 20200302

<h4>Background</h4>Seed size and number are important plant traits from an ecological and horticultural/agronomic perspective. However, in small-seeded species such as <i>Arabidopsis thaliana</i>, research on seed size and number is limited by the absence of suitable high throughput phenotyping methods.<h4>Results</h4>We report on the development of a high throughput method for counting seeds and measuring individual seed sizes. The method uses a large-particle flow cytometer to count individual ...[more]

PMID: 32158493

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Project description:Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.

Dataset Information

A high throughput method for quantifying number and size distribution of Arabidopsis seeds using large particle flow cytometry.

Publications

A high throughput method for quantifying number and size distribution of <i>Arabidopsis</i> seeds using large particle flow cytometry.

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